






"""simple docstring"""







def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      []
       for data in source_data:
              for i, el in enumerate(a_		):
                     if len(a_		) < i + 1:
                            data_lists.append([]		)
                     data_lists[i].append(float(a_		)		)
       return data_lists






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      []
       for dlist, weight in zip(a_						,   a_		):
              UpperCAmelCase						     =      min(a_		)
              UpperCAmelCase						     =      max(a_		)

              UpperCAmelCase						     =      []
              # for weight 0 score is 1 - actual score
              if weight == 0:
                     for item in dlist:
                            try:
                                   score.append(1 - ((item - mind) / (maxd - mind))		)
                            except ZeroDivisionError:
                                   score.append(1		)

              elif weight == 1:
                     for item in dlist:
                            try:
                                   score.append((item - mind) / (maxd - mind)		)
                            except ZeroDivisionError:
                                   score.append(0		)

                            # weight not 0 or 1
              else:
                     UpperCAmelCase						     =      F'''Invalid weight of {weight:f} provided'''
                     raise ValueError(a_		)

              score_lists.append(a_		)

       return score_lists






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      [0 for i in range(len(score_lists[0]		)		)]

       for slist in score_lists:
              for j, ele in enumerate(a_		):
                     UpperCAmelCase						     =      final_scores[j] + ele

       return final_scores






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      get_data(a_		)
       UpperCAmelCase						     =      calculate_each_score(a_						,   a_		)
       UpperCAmelCase						     =      generate_final_scores(a_		)

       # append scores to source data
       for i, ele in enumerate(a_		):
              source_data[i].append(a_		)

       return source_data






'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



